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"Karena adanya teknologi finansial yang semakin berkembang, yang berarti perpaduan keuangan dan teknologi, telah menyebar di industri keuangan, berbagai perubahan telah diantisipasi, seperti munculnya jenis layanan keuangan dan perusahaan keuangan. Menyediakan layanan keuangan, seperti pembayaran, pengiriman uang, dan pialang, perusahaan pembiayaan itu telah menyediakan, kepada perusahaan telekomunikasi dan perusahaan IT baru dengan cara baru. K-bank, Bank Kakao, dll. Kami telah bekerja pada pengembangan teknologi baru, bentuk-bentuk baru dari jasa keuangan dan produk seperti bio-sertifikasi, robot-penasihat, internet banking. Di seluruh dunia, PinTech menarik perhatian sebagai mesin pertumbuhan baru industri keuangan bersama dengan revolusi industri ke-4 dan pasar diperkirakan akan tumbuh menjadi sekitar 800 triliun won pada 2017 Gartner, 2016. Namun, konvergensi teknologi dan keuangan berarti bahwa layanan keuangan secara dramatis.

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As the Financial technology, which means the fusion of finance and technology, has been spreading in the financial industry, a variety of changes have been anticipated, such as the emergence of new types of financial services and financial companies. Providing financial services, such as payment, remittance, and brokerage, that the finance company has been providing, to telecommunication companies and new IT companies in new ways. With the development of new technology, new forms of financial services and products such as bio certification, robot advisor, internet banking led by non financial companies such as K bank, kakao bank, etc. Worldwide, financial technology is attracting attention as a new growth engine of the financial industry along with the 4th industrial revolution and the market is expected to grow to about 800 trillion Korean won by 2017 Gartner, 2016. However, the convergence of technology and finance generally means that existing services in the financial sector are dramatically streamlining or new financial services are emerging."

"ABSTRAKBelum ada peraturan yang mengharuskan perusahaan di luar industri keuangandan perbankan untuk menerapkan manajemen risiko, karenanya saat ini PT XYZbelum memiliki pengukuran risiko khususnya risiko operasional. Namun perusahaanmenyadari bahwa retail minimarket modern yang dikelolanya memilikipotensi risiko operasional yang besar yang mungkin dihadapinya di kemudianhari, maka ada keinginan kuat dari perusahaan untuk menerapkan manajemenrisiko. Penelitian ini dilakukan untuk mengidentifikasi, mengukur cadangankerugian risiko operasional (operational Value at Risk), dan memitigasi risiko operasionalyang mungkin dialami oleh PT. XYZ. Dari proses indentifikasi ditemukanbahwa risiko operasional yang paling signifikan terhadap bisnis perusahaan adalahrisiko hilangnya persediaan barang dagangan di toko ritel minimarket. PengukuranOpVaR dilakukan dengan menggunakan metode loss distribution approach menghasilkanangka sebesar Rp. 11.885.041.054 dan dinyatakan valid setelah dilakukanbacktesting.

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ABSTRACTThere are no regulations requiring companies outside the financial and bankingindustry to implement risk management, hence the current PT. XYZ does not havea particular risk measurement of operational risk. However, the company realizedthat retail modern minimarket has great potential operational risks that may be encounteredin the future, then there is a strong desire of companies to implement riskmanagement. This study was conducted to identify, measure operational risk lossreserves or Operational Value at Risk, and mitigate operational risks that may beexperienced by PT. XYZ. From the identification process, this study finds that themost significant operational risks to the company?s business is the risk of loss ofmerchandise supply in a retail minimarket store. OpVaR measurements performedusing the loss distribution approach amounted to IDR 11,885,041,054 and declaredvalid after backtesting."

"Akhirnya kita tiba di era kecerdasan buatan di mana teknologi telah dilatih untuk meniru kecerdasan manusia. Machine Learning adalah salah satu terobosan dalam kecerdasan buatan yang menyajikan banyak potensi untuk menghadirkan keunggulan kompetitif dengan kemampuannya untuk mengoptimalkan analisis data secara otomatis. Industri perbankan komersial selalu menjadi pengadopsi awal berbagai kecerdasan buatan. Namun, potensi Machine Learning di perbankan komersial masih belum tergali. Lama setelah krisis keuangan yang hebat, industri perbankan komersial telah menjadi lebih besar dan lebih kompetitif, dengan banyak pengganggu yang mengubah lingkungan persaingan di industri tersebut. Industri perbankan kini memasuki era transformasi digital berikutnya, di mana persaingan semakin ditentukan oleh teknologi. Di bank komersial, layanan pelanggan adalah area krusial di mana semua titik kontak langsung dengan pelanggan terjadi. Bank harus terus mencari cara baru dalam meningkatkan kemampuannya dalam memberikan layanan berkualitas tinggi yang memenuhi bahkan melebihi harapan nasabah. Untuk mencapai hal tersebut diperlukan pengetahuan yang intensif tentang pelanggan. Makalah ini bertujuan untuk membahas potensi machine learning dalam meningkatkan berbagai aktivitas customer service di bank umum. Secara khusus, Machine Laerning meningkatkan pengembangan layanan yang dipersonalisasi, pencegahan penipuan, dan bantuan pelanggan virtual yang sangat penting untuk kelangsungan hidup bank komersial saat ini.

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We have finally arrived in the age of artificial intelligence where technologies have been trained to imitate human intelligence. Machine learning is one of the breakthroughs in artificial intelligence that serve a lot of potential to bring competitive advantage with its ability to automatically optimize data analyses. Commercial banking industry has always been the early adopter of various artificial intelligence. Yet, the potentials of machine learning in commercial banking are still unexplored. As banking industry is now entering the next era of digital transformation, the competition is increasingly defined by technology. Technological transformation has changed the competitive environment in banking industry, and influenced consumer behaviour. Machine learning offers new ways in which banks could overcome these challenges. In commercial banking, customer service is the crucial area where all direct touch-points with customers take place. Banks must continuously find new ways in improving its ability to deliver high quality service that meets and even exceeds customer expectation. The objective of this paper is to discuss the potential of machine learning in improving various customer service activities in a commercial bank. This paper is particularly relevance for managers in banking industry as it provides comprehensive discussion about the business implications of machine learning. The research question that I aim to answer in this paper is about: How Does Machine Learning Help Commercial Banks to Sustain Competitive Advantage in Customer Service?"

"Target PT XYZ yang bergerak di industri keuangan untuk menjadi perusahaan terkemuka di Asia Tenggara telah didukung oleh lebih dari 200 juta data pelanggan yang ada di core system-nya. Data dengan jumlah yang sangat besar tersebut diharapkan dapat menciptakan peluang bisnis, membangun budaya sadar risiko dan menambah keunggulan dalam strategi bisnis PT XYZ. Hal tersebut dapat tercapai jika data yang digunakan adalah data yang berkualitas baik. Pada kenyataannya, ditemukan anomali pada sejumlah besar data pelanggan. Untuk dapat memberikan rekomendasi perbaikan kualitas data pelanggan, perlu dilakukan penilaian kualitas data pelanggan. Penilaian kualitas data pelanggan yang dilakukan pada penelitian ini menggunakan metode yang diperkenalkan oleh Loshin (2011). Loshin's Data Quality Management Model ini mengadopsi tingkat capability maturity model dalam penyusunan matriks karakteristiknya. Nilai kematangan yang diperoleh adalah 3,6 (expectation), 3,6 (dimension), 4,4 (policy), 3,8 (procedure), 4,2 (governance), 3,8 (standardization), 4,2 (technology), dan 3,8 (performance management). Dengan harapan senior management yang dapat mencapai level tertinggi pada kualitas data, dihasilkan 9 rekomendasi strategi. 9 rekomendasi strategi yang diajukan kepada PT XYZ merupakan hasil pemetaan antara kriteria yang belum terpenuhi dengan data quality management activity atau aktivitas DQM yang ada di Data Management Body of Knowledge (DMBOK) versi 2.0. Pengukuran dan pemantauan terhadap kualitas data yang baik menjadi rekomendasi yang paling berpengaruh untuk PT XYZ.

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PT XYZ, engaged in the financial industry, has a target to become a leading company in Southeast Asia and has been supported by more than 200 million customer data in its core system. This huge amount of data is expected to create business opportunities, build a risk-aware culture, and increase supremacy in the business strategy of PT XYZ. These things can be achieved if the data used is of good quality data. In fact, found anomalies in a large number of customer data. To get recommendations for improving the quality of customer data, it is necessary to assess the quality of customer data. The assessment of the quality of customer data carried out in this study was by using the method introduced by Loshin (2011). Loshin’s Data Quality Management Model adopts a capability maturity level model in building its characteristic matrix. Maturity levels obtained are 3.6 (expectations), 3.6 (dimensions), 4.4 (policy), 3.8 (procedures), 4.2 (governance), 3.8 (standardization), 4, 2 (technology), and 3.8 (performance management). Regarding the expectation that senior management can achieve the highest level of data quality, 9 strategic recommendations were produced 9 strategy recommendations were submitted to PT XYZ is the result of mapping between criteria that have not been met with data quality management activity in Data Management Body of Knowledge (DMBOK) version 2.0. Measurement and monitoring of good data quality is the most influential recommendation for PT XYZ."

"ABSTRAKPenelitian ini melihat pengaruh dan hubungan antara return Reksa Dana Syariah (sebagai investasi dengan instrumen portofolio berbasis syariah) dengan return Reksa Dana Konvensional (sebagai investasi dengan instrumen portofolio konvensional) dengan mengambil sampel Reksa Dana jenis pendapatan tetap dan jenis campuran. Teknik analisis untuk membuktikan hubungan antara kedua return Reksa Dana Syariah dan konvensional adalah dengan pengujian Granger Causality, Vector Autoregression (VAR) dan Model Distributed Lag. Pengujian pertama dan kedua antara return Reksa Dana AAA Syariah Fund dengan BNI Dana Berbunga Dua serta antara Reksa Dana Batasa Syariah dengan Big Dana Likuid menunjukkan bukti adanya hubungan kausalitas Granger diantara kedua return. Artinya terdapat hubungan yang bersifat dua arah antara return Reksa Dana Syariah jenis campuran dengan return Reksa Dana Konvensional jenis pendapatan tetap. Pada pengujian ketiga antara return Reksa Dana BNI Dana Syariah dan AAA Bond Fund tidak terbukti adanya hubungan kausalitas namun pengujian lebih lanjut menunjukkan adanya hubungan satu arah dimana return Reksa Dana konvensional mempengaruhi return Reksa Dana Syariah dengan jenis yang sama yaitu Pendapatan tetap. Pada pengujian keempat antara return Reksa Dana Amanah Syariah Fund dan Bahana Kombinasi Arjuna tidak terbukti adanya hubungan kausalitas maupun satu arah, artinya hubungan antara kedua Reksa Dana dengan jenis yang sama yaitu campuran antara Reksa Dana Syariah dengan Reksa Dana Konvensional adalah independent atau tidak saling mempengaruhi. Penelitian ini juga menunjukkan instrumen investasi yang membentuk portofolio Reksa Dana Syariah seperti obligasi syariah masih dipengaruhi oleh pergerakan instrumen-instrumen investasi konvensional yang ada dipasar seperti suku bunga, saham dan obligasi konvensional. Return Reksa Dana selain dipengaruhi oleh pergerakan return instrumennya juga bisa dipengaruhi oleh pm-Baku investor sendiri dan gaya investasi dari manajer investasi, kemudian penentuan harga pasar wajar obligasi yang masih belum transparan merupakan persoalan tersendiri dalam menentukan Nilai Aktiva Bersih (NAB) Reksa Dana yang pada akhirnya akan mempengaruhi besarnya return yang dihasilkan.

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ABSTRAK

Indonesian Sharia financial industry development had shown a surprising growth. Hence, there is a need to have some alternatives investment, which can give an interesting return for investors. One of the alternatives investments is Sharia Mutual Fund. Sharia investors in Indonesia are rational investors that tend to seek investment, which give better return, where the religion issue is not the main reason for them to invest. This research tries to observe the causality and the relationship between Sharia Mutual Fund (as a Sharia-based portfolio investment instrument) and Conventional Mutual Fund (as a conventional-based portfolio investment instrument). The sampling in this research is taken from Mutual Fund of Fixed Income and Mixed Income. The Granger Causality Test, Vector Auto Regression (VAR) and Model Distributed Lag are employed as the analysis technique. The results suggest that on the first and second testing of Mutual Return of AAA Syariah Fund and BNI Dana Berbunga Dua, and a testing of Batasa Syariah Mutual Fund and Big Dana Likuid proved that there was a Granger Causality Relationship between those two. It showed that there is a bi-directional causality between mixed-type Sharia Mutual Fund and Fixed-Income Conventional Mutual Fund. On the third testing of BNI Dana Syariah Mutual Fund and AAA Bond Fund, there was no causality relationship. Nevertheless, further testing had shown that there was a one-directional relationship where Mutual Fund Conventional returns give impact to the Sharia Mutual Fund returns with the same type, which is fixed income. On the fourth testing between Amanah Syariah Mutual Fund and Bahana Kombinasi Arjuna, there was no evidence of causality relationship. It showed that the relationship of Sharia Mutual Fund and Conventional Mutual Fund is independent and consistent. The results also demonstrate that investment instrument that form the Sharia Mutual Fund portfolio such as Sharia Bond, is still affected by the movement of conventional investment instruments in the market (i.e interest rate, stocks, and conventional bond). Apart from the movement of instruments return, the Mutual Fund return is also affected by the investor behavior and investment style of Fund Manager, which in turn an opaque setting of obligation fair value is still an issue in setting the Mutual Fund Net Asset Value and at the end will affect the return."

"Penelitian ini bertujuan untuk meneliti pengaruh dari kinerja ESGterhadap kinerja keuangan perusahaan berbasis akuntansi dan pasar yang ada di sektor keuangan dengan menggunakan metode regresi data panel. Penelitian ini menggunakan nilai ESG yang diperoleh dari Datastream Thomson Reuters. Sampel yang digunakan berjumlah 29 perusahaan di sektor keuangan yang berada di ASEAN-5 pada periode 2016-2021. Penelitian ini menemukan beberapa temuan yakni terdapat positif signifikan antara kinerja ESG combined score terhadap ROA; terdapat pengaruh negatif signifikan antara kinerja ESG combined score terhadap MTB; pengaruh positif signfikan antara kinerja ENV terhadap ROE; pengaruh negatif signifikan antara kinerja ENV terhadap MTB; pengaruh positif signfikan antara kinerja SOC terhadap TQ. Pilar Governance secara individu tidak berpengaruh signifikan terhadap kinerja keuangan perusahaan yang diukur menggunakan ROA, ROE, TQ, & MTB.

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This study aims to examine the effect of sustainability performance on the accounting and market-based financial performance of companies in the financial sector using the panel data regression method. This study uses ESG values obtained from the Thomson Reuters Datastream. The sample used was 29 companies in the financial sector in 5 ASEAN countries in the 2016-2021 period. This study found several findings, namely that there was a significant positive difference between ESG combined score performance on ROA; there is a significant negative effect between the combined score ESG performance on MTB; significant positive effect between ENV performance on ROE; significant negative effect between ENV performance on MTB; significant positive effect between SOC performance on TQ. The pillars of Governance individually have no significant effect on the company's financial performance as measured using ROA, ROE, TQ, & MTB."